# AtomDiffuser: Time-Aware Degradation Modeling for Drift and Beam Damage in STEM Imaging

arXiv: https://arxiv.org/abs/2508.10359 · HTML: https://mlsanity.com/paper/2508.10359/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise](https://mlsanity.com/paper/2208.09392/) (2022)
- [Denoising Diffusion Bridge Models](https://mlsanity.com/paper/2309.16948/) (2023)
- [Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models](https://mlsanity.com/paper/2305.15779/) (2023)
- [AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond](https://mlsanity.com/paper/2105.07485/) (2021)
- [Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling](https://mlsanity.com/paper/2308.02165/) (2023)
- [nnMobileNet: Rethinking CNN for Retinopathy Research](https://mlsanity.com/paper/2306.01289/) (2023)
- [FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion](https://mlsanity.com/paper/2403.03463/) (2024)
- [Addressing Class Imbalance and Data Limitations in Advanced Node Semiconductor Defect Inspection: A Generative Approach for SEM Images](https://mlsanity.com/paper/2407.10348/) (2024)
- [Recovering a Molecule's 3D Dynamics from Liquid-phase Electron Microscopy Movies](https://mlsanity.com/paper/2308.11927/) (2023)
- [Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification](https://mlsanity.com/paper/2412.02825/) (2024)
- [Improving Multislice Electron Ptychography with a Generative Prior](https://mlsanity.com/paper/2507.17800/) (2025)
- [Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion](https://mlsanity.com/paper/2501.00944/) (2025)

## Abstract (arXiv)

Scanning transmission electron microscopy (STEM) plays a critical role in modern materials science, enabling direct imaging of atomic structures and their evolution under external interferences. However, interpreting time-resolved STEM data remains challenging due to two entangled degradation effects: spatial drift caused by mechanical and thermal instabilities, and beam-induced signal loss resulting from radiation damage. These factors distort both geometry and intensity in complex, temporally correlated ways, making it difficult for existing methods to explicitly separate their effects or model material dynamics at atomic resolution. In this work, we present AtomDiffuser, a time-aware degradation modeling framework that disentangles sample drift and radiometric attenuation by predicting an affine transformation and a spatially varying decay map between any two STEM frames. Unlike traditional denoising or registration pipelines, our method leverages degradation as a physically heuristic, temporally conditioned process, enabling interpretable structural evolutions across time. Trained on synthetic degradation processes, AtomDiffuser also generalizes well to real-world cryo-STEM data. It further supports high-resolution degradation inference and drift alignment, offering tools for visualizing and quantifying degradation patterns that correlate with radiation-induced atomic instabilities.